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source("tianfengRwrappers.R")

载入需要的程辑包：dplyr

载入程辑包：‘dplyr’

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    count

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    combine

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    intersect, setdiff, union

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载入需要的程辑包：reticulate
载入需要的程辑包：tidyr

载入程辑包：‘tidyr’

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    expand


载入程辑包：‘MySeuratWrappers’

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    DimPlot, DoHeatmap, LabelClusters, RidgePlot, VlnPlot


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载入需要的程辑包：viridisLite

载入程辑包：‘reshape2’

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NOTE: Either Arial Narrow or Roboto Condensed fonts are required to use these themes.
      Please use hrbrthemes::import_roboto_condensed() to install Roboto Condensed and
      if Arial Narrow is not on your system, please see https://bit.ly/arialnarrow

Registered S3 method overwritten by 'enrichplot':
  method               from
  fortify.enrichResult DOSE
clusterProfiler v3.14.3  For help: https://guangchuangyu.github.io/software/clusterProfiler

If you use clusterProfiler in published research, please cite:
Guangchuang Yu, Li-Gen Wang, Yanyan Han, Qing-Yu He. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS: A Journal of Integrative Biology. 2012, 16(5):284-287.

载入程辑包：‘clusterProfiler’

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    simplify

Registering fonts with R

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    last_plot

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载入程辑包：‘DT’

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========================================
circlize version 0.4.13
CRAN page: https://cran.r-project.org/package=circlize
Github page: https://github.com/jokergoo/circlize
Documentation: https://jokergoo.github.io/circlize_book/book/

If you use it in published research, please cite:
Gu, Z. circlize implements and enhances circular visualization
  in R. Bioinformatics 2014.

This message can be suppressed by:
  suppressPackageStartupMessages(library(circlize))
========================================

载入需要的程辑包：grid
========================================
ComplexHeatmap version 2.2.0
Bioconductor page: http://bioconductor.org/packages/ComplexHeatmap/
Github page: https://github.com/jokergoo/ComplexHeatmap
Documentation: http://jokergoo.github.io/ComplexHeatmap-reference

If you use it in published research, please cite:
Gu, Z. Complex heatmaps reveal patterns and correlations in multidimensional 
  genomic data. Bioinformatics 2016.
========================================


载入程辑包：‘ComplexHeatmap’

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    add_heatmap

source("XGBoost_wrapper.R")

载入程辑包：‘xgboost’

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    __  ___________    __  _____________
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  / /|_/ / /   / /   / / / /\__ \ / /   
 / /  / / /___/ /___/ /_/ /___/ // /    
/_/  /_/\____/_____/\____//____//_/    version 5.4.9
Type 'citation("mclust")' for citing this R package in publications.
Registered S3 methods overwritten by 'dbplyr':
  method         from
  print.tbl_lazy     
  print.tbl_sql      
Registered S3 method overwritten by 'cli':
  method     from    
  print.boxx spatstat
─ Attaching packages ──────────────────────────────── tidyverse 1.3.1 ─
✓ tibble  3.1.5     ✓ stringr 1.4.0
✓ readr   2.1.2     ✓ forcats 0.5.1
✓ purrr   0.3.4     
─ Conflicts ───────────────────────────────── tidyverse_conflicts() ─
x dplyr::collapse()   masks IRanges::collapse()
x dplyr::combine()    masks Biobase::combine(), BiocGenerics::combine()
x dplyr::count()      masks matrixStats::count()
x dplyr::desc()       masks IRanges::desc()
x Matrix::expand()    masks tidyr::expand(), S4Vectors::expand()
x plotly::filter()    masks dplyr::filter(), stats::filter()
x dplyr::first()      masks S4Vectors::first()
x widgetTools::funs() masks dplyr::funs()
x dplyr::lag()        masks stats::lag()
x purrr::map()        masks mclust::map()
x Matrix::pack()      masks tidyr::pack()
x ggplot2::Position() masks BiocGenerics::Position(), base::Position()
x purrr::reduce()     masks GenomicRanges::reduce(), IRanges::reduce()
x plotly::rename()    masks dplyr::rename(), S4Vectors::rename()
x purrr::simplify()   masks clusterProfiler::simplify(), DelayedArray::simplify()
x xgboost::slice()    masks plotly::slice(), dplyr::slice(), IRanges::slice()
x Matrix::unpack()    masks tidyr::unpack()

载入程辑包：‘lambda.r’

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    %as%

Creating a generic function for ‘toJSON’ from package ‘jsonlite’ in package ‘googleVis’

harmony

library(harmony)
Idents(ds0) <- "ds0"
Idents(ds1) <- "ds1"
Idents(ds2) <- "ds2"
CAD_merge <- merge(ds0,c(ds1,ds2))
CAD_merge$orig.ident <- Idents(CAD_merge) #存储原始分区

CAD_merge <- CAD_merge %>% 
  PercentageFeatureSet(pattern = "^MT-", col.name = "percent.mt") %>%
  SCTransform(vars.to.regress = "percent.mt", verbose = F) %>% 
  RunPCA()

# saveRDS(CAD_merge, "CAD_merge.rds")

CAD_merge_harmony <- RunHarmony(CAD_merge, "orig.ident", plot_convergence = T, 
                                project.dim = T, assay.use = "SCT")
CAD_merge_harmony@reductions[["pca"]] <- CAD_merge_harmony@reductions[["harmony"]]

CAD_merge_harmony <- CAD_merge_harmony %>% FindNeighbors(dims = 1:20) %>%
  RunUMAP(dims = 1:20) %>% 
  FindClusters(resolution = 0.1)

PCAPlot(CAD_merge_harmony, split.by = "orig.ident")
DimPlot(CAD_merge_harmony, reduction = "harmony", split.by = "orig.ident")

CAD_merge_harmony <- FindClusters(CAD_merge_harmony, resolution = 0.2)
umapplot(CAD_merge_harmony, split.by = "orig.ident")
# saveRDS(CAD_merge_harmony,"CAD_merge_harmony.rds")

亚群标记

selected_features <-intersect(FindVariableFeatures(ds1, nfeatures = 200)@assays[["SCT"]]@var.features,
                              FindVariableFeatures(ds2, nfeatures = 200)@assays[["SCT"]]@var.features) %>%
  intersect(FindVariableFeatures(ds0, nfeatures = 200)@assays[["SCT"]]@var.features)

## ds2作为ref
Idents(ds2) <- ds2$seurat_clusters
bst_model <- XGBoost_train_from_seuobj(ds2)
ds1 <- XGBoost_predict_from_seuobj(ds1, bst_model)
ds1 <- project2ref_celltype(ds1, ds2)
ds0 <- XGBoost_predict_from_seuobj(ds0, bst_model)
ds0 <- project2ref_celltype(ds0, ds2)

ref_sce <- mkref_scmap_from_seuobj(ds2)
ds1 <- query_scmap_from_refsce(ds1, ref_sce)
ds0 <- query_scmap_from_refsce(ds0, ref_sce)

umapplot(ds1, group.by = "ref_celltype")
umapplot(ds0, group.by = "ref_celltype")

umapplot(ds1, group.by = "scmap_idents")
umapplot(ds0, group.by = "scmap_idents")


ds1_scmap <- ds1$scmap_idents
ds1_xgb <- ds1$ref_celltype
ds0_scmap <- ds0$scmap_idents
ds0_xgb <- ds0$ref_celltype
ds2_ref <- ds2$Classification1

ds1_harmony <- subset(CAD_merge_harmony, orig.ident == "ds1")$ds2_celltype
ds0_harmony <- subset(CAD_merge_harmony, orig.ident == "ds0")$ds2_celltype

df <- data.frame(row.names = selected_features)
for(cell_type in c("SMC1","Fibromyocyte","SMC2","Pericyte")){
  df[paste0("ds2ref_",cell_type)] <- data.frame(FetchData(ds2, selected_features, cells = names(ds2_ref[ds2_ref == cell_type]))) %>% colMeans()
  
  df[paste0("ds1scmap",cell_type)] <- data.frame(FetchData(ds1, selected_features, cells = names(ds1_scmap[ds1_scmap == cell_type]))) %>% colMeans() 
  
  df[paste0("ds0scmap",cell_type)] <- data.frame(FetchData(ds0, selected_features, cells = names(ds0_scmap[ds0_scmap == cell_type]))) %>% colMeans()
  
  df[paste0("ds1xgb_",cell_type)] <- data.frame(FetchData(ds1, selected_features, cells = names(ds1_xgb[ds1_xgb == cell_type]))) %>% colMeans()
  
  df[paste0("ds0xgb_",cell_type)] <- data.frame(FetchData(ds0, selected_features, cells = names(ds0_xgb[ds0_xgb == cell_type]))) %>% colMeans()
  
    df[paste0("ds1harmony_",cell_type)] <- data.frame(FetchData(ds1, selected_features, cells = names(ds1_harmony[ds1_harmony == cell_type]))) %>% colMeans()
  
  df[paste0("ds0harmony_",cell_type)] <- data.frame(FetchData(ds0, selected_features, cells = names(ds0_harmony[ds0_harmony == cell_type]))) %>% colMeans()
}

##只考虑SMC2

correlation heatmap

#PCA

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